Design of highly effective multilayer feedforward neural network by using genetic algorithm

Abstract

This paper presents a highly effective and precise neural network method for choosing the activation functions (AFs) and tuning the learning parameters (LPs) of a multilayer feedforward neural network by using a genetic algorithm (GA). The performance of the neural network mainly depends on the learning algorithms and the network structure. The backpropagation learning algorithm is used for tuning the network connection weights, and the LPs are obtained by the GA to provide both fast and reliable learning. Also, the AFs of each neuron in the network are automatically chosen by a GA. The present study consists of 10 different functions to accomplish a better convergence of the desired input–output mapping. Test studies are performed to solve a set of two‐dimensional regression problems for the proposed genetic‐based neural network (GNN) and conventional neural network having sigmoid AFs and constant learning parameters. The proposed GNN has also been tested by applying it to three real problems in the fields of environment, medicine, and economics. Obtained results prove that the proposed GNN is more effective and reliable when compared with the classical neural network structure.

A Free-Space-Based Model for Predicting Peanut Moisture Content during Natural Drying

“…Te ELM is a new feedforward neural network learning algorithm that difers from the traditional gradient-based feedforward neural network learning algorithms in that the connection weights between the hidden and the input layers, as well as the thresholds of the neurons in the hidden layer [26], are generated at random. To obtain a unique optimal solution during parameter training, we set the number of buried neurons.…”